# Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scaling

arXiv: https://arxiv.org/abs/2509.16343 · HTML: https://mlsanity.com/paper/2509.16343/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 13 of 13)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [GeoChat: Grounded Large Vision-Language Model for Remote Sensing](https://mlsanity.com/paper/2311.15826/) (2023)
- [RSGPT: A Remote Sensing Vision Language Model and Benchmark](https://mlsanity.com/paper/2307.15266/) (2023)
- [Phi-4-reasoning Technical Report](https://mlsanity.com/paper/2504.21318/) (2025)
- [VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2406.12384/) (2024)
- [Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models](https://mlsanity.com/paper/2402.11622/) (2024)
- [THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large Vision-Language Models](https://mlsanity.com/paper/2405.05256/) (2024)
- [Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation](https://mlsanity.com/paper/2404.19752/) (2024)

## Abstract (arXiv)

Building robust vision systems for high-stakes domains such as remote sensing requires stronger visual reasoning than what single-pass inference typically provides; yet, retraining large models is often computationally expensive and data intensive. We present Visual Reasoning Agent (VRA), a training-free agentic visual reasoning framework that orchestrates off-the-shelf large vision-language models (LVLMs) with a large reasoning model (LRM) through an iterative Think-Critique-Act loop for cross-model verification, self-critique, and recursive refinement. On the remote sensing benchmark VRSBench VQA dataset, VRA consistently outperforms multiple standalone LVLM baselines and achieves up to 40.67% improvement on challenging question types spanning both perception and reasoning tasks. In addition, integrating three LVLMs with VRA improves the overall accuracy of the standalone LVLMs from 52.8% to 78.8%, demonstrating the effectiveness of agentic reasoning with increased inference-time compute.